The complexity of the AE is dominated by the complexity of the convolutional layers, leading to a total of \(7.29\)  millions FLOPs (\(\approx 3.65\)  millions MACCs when considering the multiply and add operations as a single instruction) for the encoder network. The reader can refer to [1]} for the details of the AE architecture, and to [2]} for the details of these computations. In this work, we propose to reduce the complexity of the convolutional layers by replacing convolutional layers with grouped convolutional layers. Hence, we take advantage of the network aggregation principle, as recommended also in [3]}. By assuming that the input of layer \(l\)  consists of \(C^{(l-1)}\)  channels and that the output of layer \(l\)  consists of \(C^{(l)}\)  channels, we have
\(X_j^{(l)} &= \sum \nolimits _{i=1}^{C^{(l-1)}}X_i^{(l-1)} \circledast K_{ij}^{(l)} + t_j^{(l)}\mathbf {1},\\X_j^{(l)} &= \sum \nolimits _{i=1}^{G_{n}^{(l-1)}}X_i^{(l-1)} \circledast K_{ij}^{(l)} + t_j^{(l)}\mathbf {1},\) 
